211 to 220 of 302 Results
May 26, 2023 - Materials Design
Gubaev, Konstantin; Zaverkin, Viktor; Srinivasan, Prashanth; Duff, Andrew; Kästner, Johannes; Grabowski, Blazej, 2023, "Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems", https://doi.org/10.18419/DARUS-3516, DaRUS, V1
Data for the publication "Performance of two complementary machine-learned potentials in modelling chemically complex systems", npj. Comp. Mat. This data set contains the datasets of structures in cfg and npz formats INCAR file which was used for VASP calculations python script for reading npz format These are essentially the 2-, 3-, and 4-componen... |
Schmitt, Marvin, 2023, "Replication Code for: Meta-Uncertainty in Bayesian Model Comparison", https://doi.org/10.18419/DARUS-3514, DaRUS, V1, UNF:6:zUDr3KGdcaDCy+jFtcz8lA== [fileUNF]
This dataverse contains the code for the paper Meta-Uncertainty in Bayesian Model Comparison: https://doi.org/10.48550/arXiv.2210.07278 Note that the R code is structured as a package, thus requiring a local installation with subsequent loading via library(MetaUncertaintyPaper). The experiments from the accompanying paper (see below) are implemente... |
May 23, 2023 - Institute of Geodesy
Tourian, Mohammad J., 2023, "Data for: Current availability and distribution of Congo basin's freshwater resources", https://doi.org/10.18419/DARUS-3377, DaRUS, V2
The Congo Basin is of global significance for biodiversity and the water and carbon cycles. However, its freshwater availability remains highly unknown. Here, we leverage two decades of satellite and in situ observations to develop a new method that characterizes the relationship between Drainable Water Storage Anomaly (DWSA) and river discharge ac... |
May 17, 2023PN 6
Meta-Uncertainty represents a fully probabilistic framework for quantifying the uncertainty over Bayesian posterior model probabilities (PMPs) using meta-models. Meta-models integrate simulated and observed data into a predictive distribution for new PMPs and help reduce overconfidence and estimate the PMPs in future replication studies. |
May 15, 2023 - PN 4-7
Baier, Alexandra; Frank, Daniel, 2023, "deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning", https://doi.org/10.18419/DARUS-3455, DaRUS, V1
deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands: dee... |
May 15, 2023 - PN 4-7
Baier, Alexandra; Aspandi, Decky; Staab, Steffen, 2023, "Supplements for "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks""", https://doi.org/10.18419/DARUS-3457, DaRUS, V1
This repository contains the necessary scripts to reproduce the results from our paper "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks". See the README file for more information. The most current version of this software is available on Github. |
RP11-1: Long-span Fibre Composite Structures. RP11-2: Hybrid FRP-Timber Building System and Material System Development. |
May 12, 2023 - EXC IntCDC Research Project 12 'Computational Co-Design Framework for Fibre Composite Building Systems'
Gil Pérez, Marta; Mindermann, Pascal; Zechmeister, Christoph; Forster, David; Guo, Yanan; Hügle, Sebastian; Kannenberg, Fabian; Balangé, Laura; Schwieger, Volker; Middendorf, Peter; Bischoff, Manfred; Menges, Achim; Gresser, Götz Theodor; Knippers, Jan, 2023, "Post-processed and normalized data sets for the data processing, analysis, and evaluation methods for co-design of coreless filament-wound structures", https://doi.org/10.18419/DARUS-3449, DaRUS, V1, UNF:6:3jBvTQjaf+dWmcUwcF1GkA== [fileUNF]
Post-processed and normalized data sets for specimens S2-0, S2-1, S2-2, S2-4, S2-8 and S2-9, used in Figure 14 of the publication: "Data processing, analysis, and evaluation methods for co-design of coreless filament-wound building systems", in the Journal of Computational Design and Engineering. The data allows the comparison of different geometri... |
